{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.26933163]\n",
      " [0.40790081]\n",
      " [0.41134019]\n",
      " [0.4919778 ]\n",
      " [0.53951416]\n",
      " [0.75625119]\n",
      " [0.85314708]\n",
      " [1.28998441]\n",
      " [1.34177688]\n",
      " [1.43102009]\n",
      " [1.45234053]\n",
      " [1.56290545]\n",
      " [1.57726741]\n",
      " [1.73127538]\n",
      " [1.78734401]\n",
      " [2.1451259 ]\n",
      " [2.4998014 ]\n",
      " [2.60032736]\n",
      " [2.67816958]\n",
      " [2.83322301]\n",
      " [2.9107834 ]\n",
      " [2.98589028]\n",
      " [3.03516026]\n",
      " [3.19732179]\n",
      " [3.52639219]\n",
      " [3.69923117]\n",
      " [3.70068126]\n",
      " [3.7488366 ]\n",
      " [3.77154945]\n",
      " [3.77464121]\n",
      " [3.97090347]\n",
      " [4.11806282]\n",
      " [4.13026666]\n",
      " [4.47325016]\n",
      " [4.53079604]\n",
      " [4.54591633]\n",
      " [4.5619764 ]\n",
      " [4.68496094]\n",
      " [4.71267237]\n",
      " [4.80671171]]\n",
      "[ 0.0716657   0.16180742  0.16446947  0.23239557  0.27715879  0.51893906\n",
      "  0.64272151  1.23945668  1.30674238  1.41706363  1.44216298  1.56285679\n",
      "  1.57723439  1.70902996  1.74560065  1.80095486  1.49645921  1.33974283\n",
      "  1.19717597  0.85989902  0.6658865   0.46303398  0.32242981 -0.17809178\n",
      " -1.32371362 -1.95757374 -1.9628924  -2.13910975 -2.22185693 -2.23309786\n",
      " -2.92840661 -3.41192214 -3.45000218 -4.34595183 -4.45629734 -4.48307085\n",
      " -4.51046867 -4.68319881 -4.71267219 -4.78534543]\n"
     ]
    }
   ],
   "source": [
    "# #############################################################################\n",
    "import numpy as np\n",
    "# 生成样本数据\n",
    "X = np.sort(5 * np.random.rand(40, 1), axis=0)\n",
    "print(X)\n",
    "y = (X*np.sin(X)).ravel()\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.53524523 0.74799688 0.42936999 0.03874378 0.15669795 0.64684935\n",
      " 0.18890084 0.49702264]\n"
     ]
    }
   ],
   "source": [
    "# #############################################################################\n",
    "print(np.random.rand(8)) # 给目标增加噪音\n",
    "y[::5] += 3 * (0.5 - np.random.rand(8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 3 4 5]\n",
      "[ 4  5  6  7  8  9 10 11 12]\n",
      "[ 2  3  4  5  6  7  8  9 10]\n",
      "[ 4  5  6  7  8  9 10]\n"
     ]
    }
   ],
   "source": [
    "z=np.array([1,2,3,4,5,6,7,8,9,10,11,12])\n",
    "# array([:])示例\n",
    "print(z[1:5]) # 打印index为1~5的数组，范围是左闭右开\n",
    "print(z[3:]) # 打印index=3之后的数组，包含index=3\n",
    "print(z[1:-2]) # 打印index=1到倒数第2个index之间的数组\n",
    "print(z[-9:-2]) # 打印倒数第9个index和倒数第2个index之间的数组，左闭右开"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2  5  8 11]\n",
      "[ 1  4  7 10]\n",
      "[ 4  5  6  7  8  9 10 11 12]\n",
      "[12 11 10  9  8  7  6  5  4  3  2  1]\n",
      "[12  9  6  3]\n",
      "[8 7 6 5 4]\n"
     ]
    }
   ],
   "source": [
    "# array([::])示例\n",
    "print(z[1::3]) # 以index=1为起始位置，间隔3\n",
    "print(z[::3]) # 默认从index=0开始，间隔3\n",
    "print(z[3::]) # 和[3:]一样\n",
    "print(z[::-1]) # 反向打印数据，从最后一个index开始，间隔为1\n",
    "print(z[::-3]) # 反向打印数据，从最后一个index开始，间隔为3\n",
    "print(z[7:2:-1]) # 反向打印index=2(不包含)到index=7之间的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #############################################################################\n",
    "from sklearn.svm import SVR\n",
    "# 拟合回归模型\n",
    "# C: float, default=1.0\n",
    "# 正则化参数. 正则化的强度与C成反比. 必须严格为正.\n",
    "# gamma: {‘scale’, ‘auto’} or float, default=’scale’\n",
    "# ‘rbf’, ‘poly’ and ‘sigmoid’的内核系数.\n",
    "## gamma='scale': gamma=1 / (n_features * X.var())\n",
    "## gamma=‘auto’: gamma=1 / n_features.\n",
    "# epsilon: float, default=0.1\n",
    "# epsilon-SVR模型中的Epsilon. 它指定在训练损失函数中没有惩罚的epsilon管, 该点与实际值之间的距离为epsilon.\n",
    "svr_rbf = SVR(kernel='rbf', C=100, gamma=0.1, epsilon=.1)\n",
    "svr_poly = SVR(kernel='poly', C=100, gamma='auto', degree=3, epsilon=.1,\n",
    "               coef0=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #############################################################################\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False # 用来正常显示负号\n",
    "# 查看结果\n",
    "lw = 2\n",
    "\n",
    "svrs = [svr_rbf, svr_poly]\n",
    "kernel_label = ['RBF', '多项式']\n",
    "model_color = ['m', 'g']\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 10), sharey=True) # y轴将在所有子图中共享\n",
    "for ix, svr in enumerate(svrs):\n",
    "    axes[ix].plot(X, svr.fit(X, y).predict(X), color=model_color[ix], lw=lw,\n",
    "                  label='{}模型'.format(kernel_label[ix])) # lw(linewidth)\n",
    "    axes[ix].scatter(X[svr.support_], y[svr.support_], facecolor=\"none\",\n",
    "                     edgecolor=model_color[ix], s=50,\n",
    "                     label='{}支持向量'.format(kernel_label[ix]))\n",
    "    axes[ix].scatter(X[np.setdiff1d(np.arange(len(X)), svr.support_)],\n",
    "                     y[np.setdiff1d(np.arange(len(X)), svr.support_)],\n",
    "                     facecolor=\"none\", edgecolor=\"k\", s=50,\n",
    "                     label='其他训练数据')\n",
    "    axes[ix].legend(loc='upper center', bbox_to_anchor=(0.5, 1.1),\n",
    "                    ncol=1, fancybox=True, shadow=True) # 设置图例将锚定到的bbox, 在构成图例背景的FancyBboxPatch周围启用圆形边缘.\n",
    "\n",
    "fig.text(0.5, 0.04, '数据', ha='center', va='center') # ha(horizontalalignment), va(verticalalignment)\n",
    "fig.text(0.06, 0.5, '目标', ha='center', va='center', rotation='vertical')\n",
    "fig.suptitle(\"支持向量回归\", fontsize=14)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
